Organization:Google Research
From HandWiki
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| Founded | Script error: No such module "Date time". |
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| Founder | |
| Type | Division |
| Location |
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| Services | Computer science, Artificial intelligence |
Parent organization | Alphabet Inc. |
| Website | research.google |
Google Research (also known as Research at Google) is the research division of Google, a subsidiary of Alphabet Inc.. According to its official website, Google Research publishes findings, releases open-source software, and applies research results within Google products and services as well as within the wider scientific community.[1]
Notable contributions
- The 2017 landmark paper Attention Is All You Need, which introduced the Transformer architecture, which has subsequently been used to build modern large language models.[2]
- Advances in neural machine translation powering Google Translate.[3]
- Time series forecasting.[4]
- Development of scalable learning systems and infrastructure for large-model training.[5][self-published source?]
- Flood forecasting.[6]
- Research into computational discovery via Google Accelerated Science including demonstrating the first below-threshold quantum calculations.[7]
See also
References
- ↑ "Research at Google". Google Research. https://research.google/.
- ↑ Maxwell, Hugh; Langley, Thomas. "Google's top AI researchers, including all the authors behind a landmark paper, have left for competitors. Here's where they are now." (in en-US). https://www.businessinsider.com/google-ai-teams-brain-drain-researchers-leave-2023-3.
- ↑ “Google’s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation”, Google Research, 2016. Retrieved from https://research.google/pubs/googles-neural-machine-translation-system-bridging-the-gap-between-human-and-machine-translation/
- ↑ "google-research/timesfm". GitHub, Inc.. https://github.com/google-research/timesfm.
- ↑ Kurian G, Sardashti S, Sims R, Berger F, Holt G, Li Y, Willcock J, Wang K, Quiroz H, Salem A, Grady J. (2025). “Scalable Machine Learning Training Infrastructure for Online Ads Recommendation and Auction Scoring Modeling at Google”. arXiv. Retrieved from https://arxiv.org/abs/2501.10546
- ↑ "Flood Forecasting". Google LLC. https://sites.research.google/gr/floodforecasting/.
- ↑ Castelvecchi, Davide (2024-12-09). "'A truly remarkable breakthrough': Google's new quantum chip achieves accuracy milestone" (in en). Nature 636 (8043): 527–528. doi:10.1038/d41586-024-04028-3. ISSN 1476-4687. PMID 39653720. Bibcode: 2024Natur.636..527C. https://www.nature.com/articles/d41586-024-04028-3.
External links
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